Understanding AI Algorithms
Artificial Intelligence relies on a core set of algorithms to solve problems ranging from game playing to data classification. This section enables you to visualize these "black box" algorithms, making their decision-making processes transparent and understandable.
We cover three main categories:
1. Unsupervised Learning: Algorithms like K-Means Clustering effectively group
unlabeled data, revealing hidden patterns.
2. Supervised Learning: Algorithms like KNN and Linear Regression use labeled
training data to predict outcomes for new inputs.
3. Search & Game Theory: The Minimax algorithm demonstrates how AI agents can make
optimal decisions in competitive environments like Chess or Tic-Tac-Toe.
By stepping through these visualizers, you can see exactly how weights are adjusted in a Perceptron or how centroids move in K-Means, bridging the gap between theoretical math and practical implementation.
K-Means Clustering
Visualize how unsupervised learning groups data points into clusters using iterative centroid updates.
KNN Classification
Visualize how K-Nearest Neighbors classifies new data points based on their spatial proximity to training data.
Linear Regression
Learn how Gradient Descent finds the best-fit line to predict trends in continuous data.
Perceptron Visualizer
Learn the fundamental unit of deep learning by visualizing how a single neuron classifies data.
Minimax & Alpha-Beta
Visualize how AI searches decision trees and uses Alpha-Beta pruning to optimize games.